\section{Experimental study}
\label{sec:Experimental}
For the experimentation, we generated the scenario using YAES ~\cite{Boloni-2005-YAES} library package. In the scenario, we considered static sensor nodes that are capable of wireless communication with other sensor nodes that are in a communication radii. The wireless sensor network consists of the static sensor nodes and a static base station(sink node). There exist mobile intruders in the form of animals, friendly mobile nodes and enemy mobile nodes.
Figure~\ref{Intruders} demonstrated this simulated scenario with different types of intruder models. All the sensor nodes including agents cover a definite circular range in the environment. In neuroevolution, proper encoding of chromosomes out of the existing network is required which reflects the connectivity among the sensor nodes. Evolution is performed by defining chromosomes attributed in the NEAT properties file. Chromosome speciation performed chromosome length variation by including sufficient factor of add.connection.mutation.rate and remove.connection.mutation.rate. Most important calculations require identification of fitness function for this evolution. Here, fitness of a communication path is measured by calculating connectivity, probability of intrusion and energy cost(hop count) associated with the chromosome. Disallowed re-currency, fully connected topology and roulette wheel selection of fittest communication path(route) is defined in the NEAT.

\begin{figure}
\centering
\includegraphics[width=3.5in]{Figures/scenerioImg2}
\caption{Simulation of a wireless sensor network model and different types of intruder models in a uniform area.}
\label{Intruders}
\end{figure}

The static sensor nodes are distributed in a 2D area according to their communication ranges. For non-uniform 2D areas including obstacles, sensor nodes that have positions on the obstacles are removed from the network, and connectivity is provided by the nodes that have positions in available locations. Figure~\ref{Obstacles} shows a simulation of communication in a wireless sensor network in a 2D non-uniform area including two obstacles. In this figure, animals represent intruders in our model.
\begin{figure}
\centering
\includegraphics[width=3.5in]{Figures/scenerioImg}
\caption{Simulation of a wireless sensor network model and intruders in a non-uniform area including two obstacles.}
\label{Obstacles}
\end{figure}

Intruders are mobile and have a specific movement pattern. For simplicity purposes, we used a simple mobility model, although one can choose an advanced and scenario-specific mobility model for accurate results, among many mobility models used in literature of networks with mobile elements. In our simple mobility model, there is a start point in the area for movement of all intruders, and an end point. After the intruders start from the same start point, there are multiple paths available from the start point to the end point for the intruders to select for movement. Intruders make the selection of the path to move randomly according to uniform distribution. Paths are specified manually as trajectory lines and have some intersection points with the communication links in the wireless sensor network. We assume all intruders have antennas to be able to receive the signals between pairs of nodes. An intruder is able to receive the signal whenever it intersects with the corresponding communication link at the same time of data transfer between the nodes.

YAES~\cite{Boloni-2005-YAES} simulator is used in order to generate synthetic mobility traces of intruders in a 2D area. Using this simulation we also calculate the following probabilities. For all intruders, communication link that the intruder intersects with and intersection time is stored to calculate the probability for each communication link. For each communication link, we calculate the probability of an intruder intersecting with this link by simply dividing the time portion of intrusion by the total simulation time. These probabilities are used for calculating the fitness score of a genome, including genes that correspond to communication links.

ANJI (Another NEAT Java Implementation) which is a Java implementation of NEAT is used to configure experiments. All the parameters are defined in the property file. These parameters define the nature of the experiment such as number of generation, population size, mutation rate, selection criteria, stimulus size, response size, fitness function, activation function, connection topology and many more. All these parameters require a specific configuration of values to successful evaluate a solution.
We started experimentation with population size of 100 with 100 generations, 0.03 of mutation rate and roulette wheel selection criteria.
No comparative study or performance evaluation is provided here because we found no satisfactory generation of genome during our experimentations. Reasons for this discrepancy could have been improper set of parameter values. We would like to try to experiment over different parameter settings to get better simulation results in future.



